Feature attributions are often used to identify the top-\(k\)  most important features for a given model outcome [1]}. However, we argue that these feature sets may not be explanatory with respect to a given prediction.
To show this, we compute R2I and I2R sufficiency – i.e., \(PS(c, y)\)  and \(PS(1 - c, 1 - y)\) , respectively – for the top-\(k\)  most influential features (\(k \in [1,9]\) ) as identified by SHAP [2]} and LENS.
Fig. REF  shows results from the R2I setting for German credit [3]} and SpamAssassin datasets [4]}.
Our method attains higher \(PS\)  for all cardinalities. We repeat the experiment over 50 inputs, plotting means and 95% confidence intervals for all \(k\) .
Results indicate that our ranking procedure delivers more informative explanations than SHAP at any fixed degree of sparsity.
Results from the I2R setting are in Appendix .
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